- Open Access
Quantitative proteomics identification of phosphoglycerate mutase 1 as a novel therapeutic target in hepatocellular carcinoma
- Fenglian Ren†1,
- Hong Wu†2,
- Yunlong Lei†1,
- Haiyuan Zhang3,
- Rui Liu1,
- Yong Zhao1,
- Xiancheng Chen1,
- Dequan Zeng1,
- Aiping Tong1,
- Lijuan Chen1,
- Yuquan Wei1 and
- Canhua Huang1Email author
© Ren et al; licensee BioMed Central Ltd. 2010
- Received: 9 November 2009
- Accepted: 19 April 2010
- Published: 19 April 2010
Hepatocellular carcinoma (HCC) is one of the most common malignancies worldwide with poor prognosis due to resistance to conventional chemotherapy and limited efficacy of radiotherapy. There is an urgent need to develop novel biomarkers for early diagnosis, as well as to identify new drug targets for therapeutic interventions.
Patients and methods
54 paired HCC samples and 21 normal liver tissues were obtained from West China Hospital of Sichuan University. Informed consent was obtained from all the patients or their relatives prior to analysis, and the project was approved by the Institutional Ethics Committee of Sichuan University. Stable Isotope Labeling with Amino Acids in Cell Culture (SILAC)-based proteomics was employed to profile the differentially expressed proteins between a HepG2 human hepatoma cell line and an immortal hepatic cell line L02. Validation of PGAM1 expression was performed by semi-quantitative RT-PCR, immunoblot and immunohistochemistry using clinical samples. shRNA expressing plasmids specifically targeting PGAM1 were designed and constructed by GenePharma Corporation (Shanghai, China), and were utilized to silence expression of PGAM1 in vitro and in vivo. Cell proliferation was measured by a combination of colony formation assay and Ki67 staining. Apoptosis was examined by flow cytometry and TUNEL assay.
A total of 63 dysregulated proteins were identified, including 51 up-regulated proteins, and 12 down-regulated proteins (over 2-fold, p < 0.01). Phosphoglycerate mutase 1 (PGAM1) was found markedly upregulated. Clinico-pathological analysis indicated that overexpression of PGAM1 was associated with 66.7% HCC, and strongly correlated with poor differentiation and decreased survival rates (p < 0.01). shRNAs-mediated repression of PGAM1 expression resulted in significant inhibition in liver cancer cell growth both in vitro and in vivo.
Our studies suggested that PGAM1 plays an important role in hepatocarcinogenesis, and should be a potential diagnostic biomarker, as well as an attractive therapeutic target for hepatocellular carcinoma.
- HepG2 Cell
- Liver Cancer Cell
- Phosphoglycerate Mutase
- Liver Cancer Cell Line HepG2
- Small Lung Cancer Cell Line
Hepatocellular carcinoma (HCC) is the fifth most common malignancy worldwide with poor prognosis, and is responsible for 600 000 deaths annually worldwide [1–5]. Many patients are diagnosed at the advanced stage and missed the best opportunity for effective therapy, such as liver resection, or transplantation. On the other hand, patients who were resected often have a high frequency of metastasis/recurrence, and postoperative 5-year survival is only 30%-40% . Moreover, liver transplantation is not applicable universally because of the shortage of organ donations and occurrence of relapse . Consequently, there is an urgent need to screen for novel therapeutic targets.
The metabolism of cancer cells differs significantly from that of normal cells . Cancer cells are able to maintain high rates of aerobic glycolysis even under the high-oxygen (20%) conditions of normal tissue culture. This property, known as the "Warburg effect", has been recognized for over 70 years [9, 10]. In this context, maintaining a high level of glycolysis is indispensable for survival and growth of cancer cells . Guided by this principle, intervention with cellular glucose utilization could lead to a significant inhibition of cell growth, induction of cell death, stimulating migration of key enzymes out of the glycolytic enzyme complexes as well [12, 13]. Recently, chemistry-based functional proteomics was applied to screen for drug target against breast cancer, and phosphoglycerate mutase 1 (PGAM1) was identified as a novel metabolic enzyme involved in breast carcinogenesis .
In adult mammals, three isozymes of PGAM are present which result from the homo- and heterodimeric combinations of two different 30-kD subunits, M and B, encoded by two different genes [15, 16]. The homodimer BB-PGAM (a brain form; PGAM1 in human), is expressed mainly in liver, kidney, and brain; the homodimer MM-PGAM (muscle-specific form; PGAM2 in human), is mainly found in the mature muscle cells; and the heterodimer MB-PGAM, mainly exists in heart [16–18]. Particularly, PGAM1, a key enzyme of the glycolytic pathway, converts 3-phosphoglycerate to 2-phosphoglycerate with 2, 3-bisphosphoglycerate (2, 3-BPG) as a cofactor of the reaction to release energy which is essential for cell growth . Several investigations demonstrated that PGAM1 was overexpressed in a variety of human cancers, including breast carcinoma [14, 20]; colorectal cancer [21, 22]; lung cancer [23, 24]; prostate cancer ; oral squamous cell carcinoma ; esophageal squamous cell carcinomas ; and also associated with certain virus infection [27, 28]. Overexpression of PGAM1 can immortalize mouse embryonic fibroblasts and promote cell proliferation, suggesting its potential oncogenic property . Furthermore, a recent study showed that a PGAM1 peptide inhibitor induced cancer cell growth arrest in breast carcinoma . Taken together, targeting the PGAM1 may be preferentially lethal to the malignant cells and have potentially broad clinical and therapeutic implications.
In the present study, we utilized a quantitative proteomic approach to profile the altered expressed proteins between a liver cancer cell line HepG2, and an immortalized human normal hepatocyte cell line L02. Of the 63 dysregulated proteins, we found that PGAM1 was significantly upregulated. Clinicopathological analyses revealed that overexpression of PGAM1 was closely associated with hepatocarcinogenesis. The data presented in this study suggested that PGAM1 could be developed as a useful diagnostic biomarker, as well as a potential therapeutic target for hepatocellular carcinoma.
Clinicopathologic features of all patients.
Normal liver tissues
45.4 ± 5.7
44.8 ± 8.5 (100.0)
Surgical pathologic staging
Cell culture, SILAC labeling and transfection
Human hepatoma cell line HepG2 was purchased from ATCC (Rockville, MD, USA) and maintained in Dulbecco's modified Eagle's medium (DMEM, Gibco, USA) supplemented with 10% fetal calf serum (Hyclone, USA), penicillin (107U/L) and streptomycin (10 mg/L) at 37°C in a 5% CO2 atmosphere. Human immortalized normal liver cell line L02 was obtained from China Cell Culture Center (Shanghai, China) and cultured in Dulbecco's modified Eagle's medium (DMEM, Gibco, USA) .
For SILAC-labelling, cells were grown in SILAC™ D-MEM (Invitrogen, USA) containing 10% v/v dialysed FBS, 2 mM L-glutamine, and either 0.1 mg/mL SILAC™ light [12C6] or SILAC™ heavy [13C6] L-lysine (Invitrogen). To ensure full incorporation of the heavy and light labeled amino acids, cells were grown for at least six cell doublings prior to analysis.
For transient transfection, HepG2 cells were seeded in 6- or 96-well culture plates at a density of 105 cells or 5000 cells/well, respectively. After incubated overnight, cells were transfected with PGAM1-shRNA (2 μg/well) or the scramble shRNA using Lipofectamine 2000 (Invitrogen, Carlsbad, CA) according to the manufacturer's instructions.
Protein preparation and SDS-PAGE separation
Equal amounts of protein from HepG2 (13C6-lysine) and L02 (12C6-lysine) cell lines were mixed (60 μg in total), boiled in SDS-PAGE sample buffer, resolved by SDS-PAGE and stained with Coomassie Brilliant Blue R-250 (Merck, Germany).
In-gel trypsin digestion
In-gel digestion was performed using mass spectrometry grade Trypsin Gold (Promega, Madison, WI, USA) according to the manufacturer's instructions. Briefly, the excised gel slices were destained twice with 100 mM NH4HCO3/50% acetonitrile at 37°C for 45 min, and dried in a centrifugal vacuum concentrator, followed by a preincubation in 10-20 μl trypsin solution for 1 h. Subsequently the digestion buffer were added (40 mM NH4HCO3/10% ACN) to cover the gel pieces and incubated overnight at 37°C. Tryptic digests were extracted twice with 50% ACN/5% trifluoroacetic acid (TFA) for 1 h each time. The combined extracts were dried in a vacuum concentrator to a final volume of 5 μl at room temperature. The samples were then subjected to mass spectrometric analysis.
Protein identification and quantitation by LC-MS/MS
Mass spectra were acquired using a LC-MS mass spectrometer (Micromass, Manchester, UK). Tryptic digests were dissolved in 20 μl of 50% ACN. The automatic scan rate was 1.0 s with an interscan delay of 0.02 s, and the voltage was operated at 3.0 KV. Spectra were accumulated until a satisfactory signal/noise ratio had been obtained with the range 400 - 1600 m/z picked out for LC-MS/MS analysis. The collision energy was varied between 18 - 57 eV depending on the mass of the precursor. Quantitation was carried out by SILCA K+ 6 R+ 10 [MD]. The MS/MS data, "pkl list" files were acquired by the software ProteinLynx 2.2.5 software (Waters), which include the mass values, the intensity and the charge of the precursor ions (parent ions with +1, +2 or +3 charges in this study). The pkl files were analyzed using the MASCOT search engine against the Swiss-Prot protein database. The search parameters were carried out as follows: Database, Swiss-Prot; taxonomy, homo sapien; enzyme, trypsin; and an allowance of one missed cleavage. Carbamidomethylation was set as a fixed modification and oxidation of methionine was variable. The peptide and fragment mass tolerance were both set at 0.2 Da. Proteins were identified at least one peptide exceeding their score threshold (p < 0.01) and with their MW and pI consistent with the gel regions from which the bands were excised indicated the 95% confidence level for the matched peptides. Protein intensity alteration (>2 fold) was defined as dysregulation.
The following primer sequences were used to detect PGAM1 transcripts: sense (5'-GCACCCACTCCCTTCATACAAT-3') and antisense (5'-ACGCAGGTTACATTCGTCTTCC-3'). Total RNA was extracted using Trizol Reagent (Invitrogen, USA). RT-PCR reaction was performed as follows: reverse transcription at 45°C for 30 min and denaturation at 94°C for 2 min; then amplification for 30 cycles at 94°C for 30 s, annealing at 54°C for 1 min, and extension at 72°C for 1.5 min, followed by a terminal elongation step at 72°C for 10 min and a final holding stage at 4°C. PCR products were resolved by 1% agarose gel electrophoresis.
Western blotting analysis
Western blotting analysis was performed as described elsewhere . Briefly, 30 μg of proteins were separated by 12% SDS-PAGE and transferred to PVDF membranes (Amersham Biosciences). After blocking overnight with TBS/T containing 0.1% Tween 20 in 5% skimmed milk at 4°C, the membranes was subsequently probed with primary antibody PGAM1 (diluted 1:1000, Abcam) for 2 h at RT and washed 3 times in TBS/T. Subsequently the membranes were incubated with secondary antibody conjugated to Horseradish Peroxidase for 2 h at RT. Immunoblot was detected by the enhanced chemiluminescence (ECL) detection system (Pierce Biotech Inc., Rockford, IL, USA).
Tissues were formalin-fixed and Paraffin-embedded, and sections were consecutively cut into 3-4 μm thickness for immunohistochemistry (IHC) analysis using a Dako EnVision System (Dako Cytomation GmbH, Hamburg, Germany Denmark) according to the manufacturer's instructions. Briefly, the paraffin-sections were dewaxed, rehydrated, and incubated in 3% H2O2 for 10 min in dark at room temperature to quench the endogenous peroxidase activity. Antigen retrieval was performed in citrate buffer (pH 6.0) using autoclave sterilizer method. Subsequently, the sections were blocked by normal rabbit or goat serum diluted in PBS (pH 7.4) for 20 min at 37°C, followed by an incubation at 4°C overnight with the primary antibodies, either goat anti-PGAM1 (diluted 1:150, Abcam, Cambridge, UK) or mouse anti-Ki67 (diluted 1:200, Santa Cruz Biotechnology, Santa Cruz, CA). After rinse in fresh PBS for 15 min, slides were incubated with horseradish peroxidase-linked rabbit anti-goat and anti-mouse antibodies at 37°C for 40 min, followed by reaction with 3,3'-diaminobenzidine substrate solution (Dako Cytomation GmbH) and counterstaining with Mayer's hematoxylin. The immunohistochemical staining was assessed by calculating the percentage of positive hepatocytes and the immunostaining intensity . Slides were examined separately by two independent pathologists with no prior knowledge of each patient's clinical and pathological parameters. Any discrepancy between the two evaluators was resolved by reevaluation and careful discussion until agreement was reached.
Cell proliferation and apoptosis assay
Upon treatment, cells were incubated at 37°C for indicated durations. Cell proliferation was measured by means of MTT assay according to the manufacturer's instructions. 20 μl MTT (2 mg/ml, Sigma, St. Louis, MO) was added in the media and incubated for another 2 h. The media was removed and formazan precipitate was dissolved in 150 μl Dimethyl Sulfoxide (DMSO, Amresco, Solon, Ohio, USA). Ten minutes later, absorbance values were measured at 595 nm wavelength on a Spectra Max M5 (MDC, Sunnyrale, VA, USA).
For colony formation assay, cells were seeded in 6-well plates at a density of 300 cells per well. Assay was performed at 24 h posttransfection, cells were cultured for another two weeks. Colonies were washed with PBS, fixed with methanol and stained with Crystal Violet (Sigma, St. Louis, MO, USA). Cells were counted under a microscope and a cluster with more than 50 cells was considered as a clone.
For flow cytometric analysis, cells were trypsinized and washed with 0.9% NaCl at 72 h post transfection, and then fixed with 70% ethanol at 4°C overnight. Cells were incubated with staining solution (5 μg/ml propidium iodide, 20 μg/ml Rnase A) in dark at room temprature for 1 h. The stained cells were analyzed on an EPICS ELITE ESP flow cytometer (Beckman Coulter, USA).
TUNEL staining was performed using terminal deoxynucleotidyl transferase (Promega Inc., Madison, WI, USA). Cells were fixed in freshly prepared 4% methanol-free formaldehyde solution in PBS (pH 7.4) for 25 minutes at 4°C, washed with fresh PBS for 10 minutes at room temperature and permeabilized in 0.2% Triton-100 solution in PBS for another 5 min. After equilibration for 10 min, the cells were incubated with rTdT buffer and observed under a fluorescence microscope, and a nucleus with bright green fluorescence staining was recorded as a TUNEL-positive event (Olympus Optical Co, Hamburg, Germany).
Tumor xenograft model and shRNA treatment
The cDNA sequence of PGAM1 was obtained from Genbank (NM_002629). Three PGAM1-specific short hairpin RNAs (shRNAs) were designed based on the rules as described elsewhere . As shown in Table S1, additional file 1, shRNA expressing plasmids specifically targeting PGAM1 (termed as PGAM1-shRNA-a, -b and -c) were constructed by GenePharma Corporation (Shanghai, China) using pGPU6/GFP/Neo vector. An unrelated shRNA sequence (HK), with no homology to any human gene, was used as a negative control (shNC).
For animal study, 6-8 weeks old female nude mice (BALB/c, 18-20 g body weight) were injected subcutaneously with HepG2 cell suspensions about 2 × 106 cells/100 μl/mouse in PBS via the right flank. When the tumor diameter reached about 6 mm, the tumor-bearing mice were randomly divided into four experimental groups (n = 5 mice/group): 1) PBS: 100 μl/mouse; 2) LIPO: Lipofectamine 2000 at 12.5 μl/100 μl of PBS; 3) shNC (negative control): 5 μg/100 μl of PBS; 4) PGAM1-shRNA-a: 5 μg/100 μl of PBS. Tail intravenous injections were performed every two days, and the tumor volumes were evaluated by the following formula: tumor volumes (mm3) = π/6 × length × width2. The tumor growth inhibition in the presence or absence of PGAM1 shRNA has been monitored for 20 days until the mice were sacrificed. The tumor tissues were formalin-fixed and paraffin-embedded for immunohistochemistry. All animals received humane care according to the Institutional Animal Care and Treatment Committee of Sichuan University.
All quantitative data were recorded as mean ± S.D. Comparisons between two groups were performed by Student's t test. Differences among multiple groups were assessed by one-way ANOVA analysis, LSD-t test. Relevance analysis of ordinal data was performed by cross χ2 test. Statistical significance was defined as p < 0.05.
Proteomic profiling of the differentially expressed proteins between HepG2 and LO2 by SILAC
Proteins identified by LC MS/MS.
Theotetical molecular mass/PIc
Macrophage migration inhibitory factor
3.1 ± 0.9
Peptidyl-prolyl cis-trans isomerase A
8.7 ± 2.2
Eukaryotic translation initiation factor 5A-1
2.1 ± 0.9
3.9 ± 1.1
Phosphatidylethanolamine-binding protein 1
2.9 ± 0.9
3.1 ± 1.2
Phosphoglycerate mutase 1
6.0 ± 1.4
14-3-3 protein zeta/delta
3.3 ± 1.2
2.9 ± 0.8
Proteasome activator complex subunit 1
6.7 ± 2.1
Enoyl-CoA hydratase, mitochondrial
2.6 ± 0.8
2.4 ± 0.5
3.7 ± 1.5
Aldo-keto reductase family 1 member C1
11.7 ± 2.7
Aldo-keto reductase family 1 member C3
8.1 ± 2.4
7.8 ± 1.9
Keratin, type I cytoskeletal 19
5.8 ± 1.9
Keratin, type I cytoskeletal 18
5.1 ± 1.7
Phosphoglycerate kinase 1
18.3 ± 4.3
Isocitrate dehydrogenase [NADP] cytoplasmic
2.6 ± 0.3
Keratin, type II cytoskeletal 8
5.1 ± 1.3
60 kDa heat shock protein, mitochondrial
10 ± 3.4
3.1 ± 1.1
Glutamate dehydrogenase 1
8.3 ± 2.6
4.7 ± 1.9
Glutathione transferase omega-1
8.3 ± 2.4
2.7 ± 0.8
2.6 ± 1.1
Acidic leucine-rich nuclear phosphoprotein 32 family member B
4.1 ± 1.4
13.5 ± 2.7
9.5 ± 2.4
8.1 ± 2.7
8.5 ± 3.1
Aldo-keto reductase family 1 member C2
14.8 ± 4.5
Aldo-keto reductase family 1 member B10
13.1 ± 3.8
Complement component 1 Q subcomponent-binding protein, mitochondrial
13.3 ± 3.7
60S ribosomal protein L6
11.3 ± 3.7
Poly(rC)-binding protein 2
10.2 ± 2.4
Heat shock 70 kDa protein 1
20.4 ± 4.8
Trifunctional enzyme subunit alpha, mitochondrial
3.8 ± 1.9
11.4 ± 3.2
Heat shock 70 kDa protein 1L
29.5 ± 5.7
Protein disulfide-isomerase A4
11.4 ± 3.6
ATP synthase subunit beta, mitochondrial
22.7 ± 4.9
Pyruvate kinase isozymes M1/M2
2.3 ± 1.4
8.8 ± 3.6
14.3 ± 4.1
3.3 ± 1.6
ATP-dependent DNA helicase 2 subunit 2
9.6 ± 3.5
Cullin-associated NEDD8-dissociated protein 1
4.1 ± 2.1
ATP-dependent DNA helicase 2 subunit 1
3.1 ± 1.6
3.8 ± 1.4
Calcium ion binding
Calpain small subunit 1
3.1 ± 1.6
Calcium ion binding
2.7 ± 0.8
Calcium ion binding
Interleukin-18 precursor (IL-18)
2.3 ± 0.6
6.2 ± 1.9
3.4 ± 1.7
Pyruvate dehydrogenase E1 component alpha subunit
3.5 ± 1.5
4.4 ± 1.7
Heat shock protein HSP 90-alpha
2.8 ± 0.5
Histone H2A type 2-A
3.7 ± 1.2
Poly(rC)-binding protein 1
2.6 ± 1.4
3.7 ± 1.8
Overexpression of PGAM1 in HCC
Overexpression of PGAM1 was correlated with poor prognosis of HCC
PGAM1 immunoreactivity in normal liver tissues and hepatocellular carcinoma tissues.
+ + +a
Normal liver tissues
2.29 ± 1.02
7.93 ± 2.58
Relevance of HCC characteristics to PGAM1 immunoreactivity: histodifferentiation to PGAM1.
+ + +a
5.73 ± 2.46
7.75 ± 2.68
9.87 ± 3.17
To assess the correlation between overexpression of PGAM1 and the survival rates, 54 patients were retrospectively studied (Fig. 3B). The five year survival rates were 55.6%, 28.6%, 18.2% for weakly positive, positive and strongly positive staining samples, respectively. In order to evaluate whether PGAM1 could be utilized as an independent prognostic factor associated with clinical outcome of HCCF, multivariate analyses were carried out using Cox proportional hazard model. The risk variables examined included PGAM1 immunoreactivity (weakly/moderately compared with strongly positive), age of patients (>50 compared with ≤50 years), histodifferentiation (weak/moderate compared with poor differentiation), and surgical pathologic staging (staging I, II, III compared with IV). Our studies suggested that PGAM1 could be developed as an independent prognostic factor for HCC.
Suppression of liver cancer cell Proliferation by PGAM1-shRNA
In a pilot study, three shRNA expressing plasmids targeting PGAM1 were designed (termed as PGAM1-shRNA-a, b, c), and their silencing effects were evaluated in HepG2 cells. Our data demonstrated that the expression of PGAM1 was remarkably reduced when HepG2 cells were treated with either PGAM1-shRNA-a or PGAM1-shRNA-b whilst no apparent silencing effect could be observed if HepG2 cells were treated with PGAM1-shRNA-c, compared with the negative control shNC (See additional file 1; Fig.S1).
Knockdown of PGAM1 expression induced cancer cell apoptosis
To examine if loss of PGAM1 expression induces apoptotic cell death, flow cytometric analysis was performed to measure the sub-G1 value of HepG2 liver cancer cell treated with PGAM1-shRNA-a. As shown in Fig 4C, a clear-cut difference was observed at 72 h posttransfection, and the apoptosis/PI positive percentage reached 48.6% for PGAM1-shRNA-a treated cells compared with 1.0%, 1.2% and 7.8% for untreated, Lipofectamine 2000 and HK-shRNA, respectively (p < 0.01). As the sub-G1 values measured by flow cytometry represent dead cells arising from both apoptosis and necrosis, a more specific TUNEL assay was applied to measure the apoptotic cells induced by PGAM1-shRNA-a. Cell nuclei with DNA strand breaks were revealed by labeling free 3'-OH termini and observed to stain dark green as viewed by fluorescence microscopy, indicating apoptosis, and were recorded as TUNEL-positive nuclei. As shown in Fig. 4D, a significant increase of TUNEL-positive nuclei was observed in the PGAM1-shRNA-a transfected cells (73.7 ± 2.01%), compared with the control groups, 2.4 ± 0.67% (Untreated control), 10.2 ± 1.34% (Lipofectamine 2000), and 15.8 ± 1.67% (NC-shRNA control) (Dunnett's t test, p < 0.01). Collectively, data obtained from diverse experiments demonstrated that suppression of PGAM1 expression resulted in massive liver cancer cell apoptosis.
To rule out the potential off-target effect, HepG2 cells were treated with another PGAM1 specific shRNA (PGAM1-shRNA-b). As shown in Fig. S2 (A-C) in additional file 1, treatment with PGAM1-shRNA-b in HepG2 cells resulted in remarkable inhibition of cell proliferation, and induction of apoptosis, which were evidenced by the observations from MTT assay, clonogenic formation assay and TUNEL assay.
PGAM1-shRNA-a inhibited xenograft tumor growth in vivo
As PGAM1 repression inhibited cancer cell growth and induced remarkable apoptotic cell death in vitro, we have particular interest to examine the potential function underlying PGAM1-shRNA-a mediated anti-tumor activity in vivo. To this end, tumor cell proliferation and apoptosis were assessed by Ki-67 immunoreactivity analysis and TUNEL assay. As shown in Fig. 5B, Ki-67 positive nuclei were decreased over 68% for control with PBS (Student's t test, p < 0.01). In contrast, TUNEL assay showed a remarkably higher percentage of TUNEL positive nuclei in PGAM1-shRNA-a treated group, relative to injection of PBS control. Our data suggested that suppression of PGAM1 expression mediated by PGAM1-shRNA-a could significantly inhibit cell proliferation and induce apoptosis in vivo.
Hepatocellular carcinoma (HCC), one of the most common malignancies worldwide, remains a major health problem with increasing incidence rates even to date [4, 35], and there is an urgent need to identify novel molecular targets for diagnosis, prognosis and treatment of HCC. In the current study, a SILAC-based quantitative proteomics approach was applied to profile the altered expressed proteins between HepG2 cells and L02 cells, resulting in identification of 63 distinct proteins with altered expression, which were associated with cell metabolism, proliferation and/or apoptosis. Among them, Profilin1, a member of profilin family, also known as PFN1, was ubiquitous and down-regulated more than 3-fold in HepG2 cells. As a tumor suppressor in breast cancer cells, PFN1 was reported to be involved in multiple cell behaviors, including cell adhesion, growth, proliferation and signal transduction [36, 37]. On the contrary, some key enzymes participated in glycolytic pathway were overexpressed in HepG2 cells, exemplified by enolase, which catalysed the conversion of 2-phosphoglycerate to phosphoenolpyruvate; Phosphoglycerate kinase 1 was overexpressed more than 18-fold which catalysed the conversion of 1,3-bisphosphoglycerate to 3-phosphoglycerate coupled with the generation of ATP. Most intriguingly, we found that phosphoglycerate mutase 1 (PGAM1) was shown an upregulation up to 6-fold. As an enzyme in glycolysis, PGAM1 was ubiquitously expressed in human, Bacillus stearothermophilus, Escherichia coli, Entamoeba histolytica, et al, functions to catalyze the interconversion of 3-phosphoglycerate and 2-phosphoglycerate with 2,3-bisphosphoglycerate (2,3-BPG) [15, 41]. A recent study revealed that PGAM1 was overexpressed in breast cancer, and suppression PGAM1 expression displayed a profound antiproliferative effect, underscoring its important role in carcinogenesis . Obviously, more extensive investigations on the functions of PGAM1 which was upregulated in HCC are required to elucidate the role of PGAM1 in hepatocarcinogenesis.
Clinico-pathological analysis indicated that overexpression of PGAM1 was associated with 66.7% HCC, and strongly correlated with poor differentiation and decreased survival rates (p < 0.01). Our studies suggested that PGAM1 has the potential to be developed as a useful diagnostic and prognostic marker for HCC. Further studies should be performed to evaluate if PGAM1 could be utilized as an independent biomarker for early diagnosis of HCC. On the other hand, silencing expression of PGAM1 significantly induced liver cancer cell apoptosis both in vitro and in vivo. Apoptosis is a major barrier that must be circumvented during malignant transformation. Cancer cells evolve to evade apoptosis so that they can escape from being cleared away by the surveillance system and can survive in the crucial tumor microenvironment, such as hypoxia and nutrition depletion . Defective apoptosis was considered as a major causative factor in the genesis and development of many human cancers, triggering tumor selective apoptosis in cancer cells exploited into a promising strategy for clinical treatment . The strong apoptosis-promoting activities mediated by PGAM1-siRNA suggested that PGAM1 would be an attractive drug target for therapeutic treatment with HCC. Further intensive studies should be conducted to pinpoint the molecular mechanisms underlying PGAM1-siRNA mediated cell death.
So far, a small but increasing number of reports were documented regarding targeting a cellular metabolic enzyme for cancer therapy , thus the current study provided new insights for potential clinical treatment of HCC using siRNA-mediated suppression of PGAM1 expression since RNAi technology has emerged as a powerful tool to silence gene expression in mammalian cells so that it could be used to investigate the gene function . Hopefully, shRNA-mediated suppression of PGAM1 expression, combined with conventional surgical resection and chemotherapy strategies, will open a new avenue for clinical treatment of hepatocellular carcinoma.
This work was supported by the National 863 High Tech Foundation (2007AA021205, 2008ZX10002-009), and Chinese NSFC (30771125).
- Chen Y, Lin MC, Yao H, Wang H, Zhang A, Yu J, Hui CK, Lau GK, He M, Sung J, Kung H: Lentivirus-Mediated RNA interference targeting enhancer of Zeste Homolog 2 inhibits hepatocellular carcinoma growth through down-regulation of Stathmin. Hepatology. 2007, 46: 200-208. 10.1002/hep.21668View ArticlePubMedGoogle Scholar
- Avila MA, Berasain C, Sangro B, Prieto J: New therapies for hepatocellular carcinoma. Oncogene. 2006, 25: 3866-3884. 10.1038/sj.onc.1209550View ArticlePubMedGoogle Scholar
- El-Serag HB, Rudolph KL: Hepatocellular carcinoma: epidemiology and molecular carcinogenesis. Gastroenterolog y. 2007, 132: 2557-2576. 10.1053/j.gastro.2007.04.061.View ArticleGoogle Scholar
- Bosch FX, Ribes J, Diaz M, Cleries R: Primary liver cancer: worldwide incidence and trends. Gastroenterology. 2004, 127: S5-S16. 10.1053/j.gastro.2004.09.011View ArticlePubMedGoogle Scholar
- Yau T, Chan P, Epstein R, Poon RT: Management of advanced hepatocellular carcinoma in the era of targeted therapy. Liver Int. 2009, 29. 10.17.View ArticlePubMedGoogle Scholar
- Budhu A, Jia HL, Forgues M, Liu CG, Goldstein D, Lam A, Zanetti KA, Ye QH, Qin LX, Croce CM, Tang ZY, Wang XW: Identification of metastasis-related microRNAs in hepatocellular carcinoma. Hepatology. 2008, 47: 897-907. 10.1002/hep.22160View ArticlePubMedGoogle Scholar
- Hwang LH: Gene therapy strategies for hepatocellular carcinoma. J Biomed Sci. 2006, 13: 453-468. 10.1007/s11373-006-9085-7View ArticlePubMedGoogle Scholar
- Dang CV, Semenza GL: Oncogenic alterations of metabolism. Trends Biochem Sci. 1999, 24: 68-72. 10.1016/S0968-0004(98)01344-9View ArticlePubMedGoogle Scholar
- Warburg O: On the origin of cancer cells. Science. 1956, 123: 309-314. 10.1126/science.123.3191.309View ArticlePubMedGoogle Scholar
- Kondoh H, Lleonart ME, Gil J, Wang J, Degan P, Peters G, Martinez D, Carnero A, Beach D: Glycolytic enzymes can modulate cellular life span. Cancer Res. 2005, 65: 177-185.PubMedGoogle Scholar
- Pelicano H, Martin DS, Xu RH, Huang P: Glycolysis inhibition for anticancer treatment. Oncogene. 2006, 25: 4633-4646. 10.1038/sj.onc.1209597View ArticlePubMedGoogle Scholar
- Inoki K, Zhu T, Guan KL: TSC2 mediates cellular energy response to control cell growth and survival. Cell. 2003, 115: 577-590. 10.1016/S0092-8674(03)00929-2View ArticlePubMedGoogle Scholar
- Engel M, Mazurek S, Eigenbrodt E, Welter C: Phosphoglycerate mutase-derived polypeptide inhibits glycolytic flux and induces cell growth arrest in tumor cell lines. J Biol Chem. 2004, 279: 35803-35812. 10.1074/jbc.M402768200View ArticlePubMedGoogle Scholar
- Evans MJ, Saghatelian A, Sorensen EJ, Cravatt BF: Target discovery in small-molecule cell-based screens by in situ proteome reactivity profiling. Nat Biotechnol. 2005, 23: 1303-1307. 10.1038/nbt1149View ArticlePubMedGoogle Scholar
- de Atauria P, Repiso A, Oliva B, Vives-Corrons JL, Climent F, Carreras J: Characterization of the first described mutation of human red blood cell phosphoglycerate mutase. Biochim Biophys Acta. 2005, 1740: 403-410.View ArticleGoogle Scholar
- Zhang J, Yu L, Fu Q, Gao J, Xie Y, Chen J, Zhang P, Liu Q, Zhao S: Mouse phosphoglycerate mutase M and B isozymes: cDNA cloning, enzyme activity assay and mapping. Gene. 2001, 264: 273-279. 10.1016/S0378-1119(00)00597-7View ArticlePubMedGoogle Scholar
- Sakoda S, Shanske S, DiMauro S, Schon EA: Isolation of a cDNA encoding the B isozyme of human phosphoglycerate mutase (PGAM) and characterization of the PGAM gene family. J Biol Chem. 1988, 263: 16899-16905.PubMedGoogle Scholar
- Betrán E, Wang W, Jin L, Long M: Evolution of the phosphoglycerate mutase processed gene in human and chimpanzee revealing the origin of a new primate gene. Mol Biol Evol. 2002, 19: 654-663.View ArticlePubMedGoogle Scholar
- Narayanan NK, Narayanan BA, Nixon DW: Resveratrol-induced cell growth inhibition and apoptosis is associated with modulation of phosphoglycerate mutase B in human prostate cancer cells: two-dimensional sodium dodecyl sulfate-polyacrylamide gel electrophoresis and mass spectrometry evaluation. Cancer Detect Prev. 2004, 28: 443-452. 10.1016/j.cdp.2004.08.009View ArticlePubMedGoogle Scholar
- Durany N, Joseph J, Jimenez OM, Climent F, Fernández PL, Rivera F, Carreras J: Phosphoglycerate mutase, 2, 3-bisphosphoglycerate phosphatase, creatine kinase and enolase activity and isoenzymes in breast carcinoma. Br J Cancer. 2000, 82: 20-27. 10.1054/bjoc.1999.0871PubMed CentralView ArticlePubMedGoogle Scholar
- Usuba T, Ishibashi Y, Okawa Y, Hirakawa T, Takada K, Ohkawa K: Purification and identification of monoubiquitin-phosphoglycerate mutase B complex from human colorectal cancer tissues. Int J Cancer. 2001, 94: 662-668. 10.1002/ijc.1524View ArticlePubMedGoogle Scholar
- Liu L, Wang S, Zhang Q, Ding Y: Identification of potential genes/proteins regulated by Tiam1 in colorectal cancer by microarray analysis and proteome analysis. Cell Biol Int. 2008, 32: 1215-1222. 10.1016/j.cellbi.2008.07.004View ArticlePubMedGoogle Scholar
- Chen G, Gharib TG, Wang H, Huang CC, Kuick R, Thomas DG, Shedden KA, Misek DE, Taylor JM, Giordano TJ, Kardia SL, Iannettoni MD, Yee J, Hogg PJ, Orringer MB, Hanash SM, Beer DG: Protein profiles associated with survival in lung adenocarcinoma. Proc Natl Acad Sci USA. 2003, 23: 13537-13542. 10.1073/pnas.2233850100.View ArticleGoogle Scholar
- Li C, Xiao Z, Chen Z, Zhang X, Li J, Wu X, Li X, Yi H, Li M, Zhu G, Liang S: Proteome analysis of human lung squamous carcinoma. Proteomics. 2006, 6: 547-558. 10.1002/pmic.200500256View ArticlePubMedGoogle Scholar
- Turhani D, Krapfenbauer K, Thurnher D, Langen H, Fountoulakis M: Identification of differentially expressed, tumor-associated proteins in oral squamous cell carcinoma by proteomic analysis. Electrophoresi. 2006, 27: 1417-1423. 10.1002/elps.200500510.View ArticleGoogle Scholar
- Fang MZ, Liu C, Song Y, Yang GY, Nie Y, Liao J, Zhao X, Shimada Y, Wang L, Yang CS: Over-expression of gastrin-releasing peptide in human esophageal squamous cell Carcinomas. Carcinogenesis. 2004, 25: 865-871. 10.1093/carcin/bgh097View ArticlePubMedGoogle Scholar
- Tong A, Wu L, Lin Q, Lau QC, Zhao X, Li J, Chen P, Chen L, Tang H, Huang C, Wei Y: Proteomic analysis of cellular protein alterations using a hepatitis B virus-producing cellular model. Proteomics. 2008, 8: 2012-2023. 10.1002/pmic.200700849View ArticlePubMedGoogle Scholar
- Kuramitsu Y, Nakamura K: Current progress in proteomic study of hepatitis C virus-related human hepatocellular carcinoma. Expert Rev Proteomics. 2005, 2: 589-601. 10.1586/147894184.108.40.2069View ArticlePubMedGoogle Scholar
- Ariizumi S, Katagiri S, Katsuragawa H, Kotera Y, Yamamoto M: Sectionectomy is suitable for patients with T2 hepatocellular carcinoma according to the modified international union against cancer TNM classification. Dig Surg. 2007, 24: 342-348. 10.1159/000107714View ArticlePubMedGoogle Scholar
- Jin X, Yang YD, Chen K, Lv ZY, Zheng L, Liu YP, Chen SH, Yu CH, Jiang XY, Zhang CY, Li YM: HDMCP uncouples yeast mitochondrial respiration and alleviates steatosis in L02 and hepG2 cells by decreasing ATP and H2O2 levels: a novel mechanism for NAFLD. J Hepatol. 2009, 50: 857-860. 10.1016/j.jhep.2008.10.034View ArticleGoogle Scholar
- Wei Y, Zhao X, Kariya Y, Teshigawara K, Uchida A: Inhibition of proliferation and induction of apoptosis by abrogation of heat-shock protein (HSP) 70 expression in tumor cells. Cancer Immunol Immunother. 1995, 40: 73-78. 10.1007/BF01520287View ArticlePubMedGoogle Scholar
- Li Z, Huang C, Bai S, Pan X, Zhou R, Wei Y, Zhao X: Prognostic evaluation of epidermal fatty acid-binding protein and calcyphosine, two proteins implicated in endometrial cancer using a proteomic approach. Int J Cancer. 2008, 123: 2377-2383. 10.1002/ijc.23808View ArticlePubMedGoogle Scholar
- Zhou L, Jiang Y, Tan A, Greenlee AR, Shen Y, Liu L, Yang Q: Silencing of N-Ras gene expression using shRNA decreases transformation efficiency and tumor growth in transformed cells induced by anti-BPDE. Toxicol Sci. 2008, 105: 286-94. 10.1093/toxsci/kfn122View ArticlePubMedGoogle Scholar
- Bai Y, Deng H, Yang Y, Zhao X, Wei Y, Xie G, Li Z, Chen X, Chen L, Wang Y, Su D, Qian Z, Zhong Q, Luo H, Yi T: VEGF-targeted short hairpin RNA inhibits intraperitoneal ovarian cancer growth in nude mice. Oncology. 2009, 77: 385-394. 10.1159/000279385View ArticlePubMedGoogle Scholar
- El-Serag HB, Marrero JA, Rudolph L, Reddy KR: Diagnosis and treatment of hepatocellular carcinoma. Gastroenterology. 2008, 134: 1752-1763. 10.1053/j.gastro.2008.02.090View ArticlePubMedGoogle Scholar
- Janke J, Schlüter K, Jandrig B, Theile M, Kölble K, Arnold W, Grinstein E, Schwartz A, Estevéz-Schwarz L, Schlag PM, Jockusch BM, Scherneck S: Suppression of tumorigenicity in breast cancer cells by the microfilament protein profilin 1. J Exp Med. 2000, 191: 1675-1686. 10.1084/jem.191.10.1675PubMed CentralView ArticlePubMedGoogle Scholar
- Wu N, Zhang W, Yang Y, Liang Y, Wang L, Jin J, Cai X, Zha X: Profilin 1 obtained by proteomic analysis in all-trans retinoic acid-treated hepatocarcinoma cell lines is involved in inhibition of cell proliferation and migration. Proteomics. 2006, 6: 6095-6106. 10.1002/pmic.200500321View ArticlePubMedGoogle Scholar
- Rigden DJ, Bagyan I, Lamani E, Setlow P, Jedrzejas MJ: A cofactor-dependent phosphoglycerate mutase homolog from Bacillus stearothermophilus is actually a broad specificity phosphatase. Protein Sci. 2001, 10: 1835-1846. 10.1110/ps.15701PubMed CentralView ArticlePubMedGoogle Scholar
- Fraser HI, Kvaratskhelia M, White MF: The two analogous phosphoglycerate mutases of Escherichia coli . FEBS Lett. 1999, 455: 344-348. 10.1016/S0014-5793(99)00910-2View ArticlePubMedGoogle Scholar
- Rafael MS, Encalada R, Alvaro MH, Saavedra E: Experimental validation of metabolic pathway modeling an illustration with glycolytic segments from Entamoeba histolytica. FEBS J. 2008, 275: 3454-3469. 10.1111/j.1742-4658.2008.06492.xView ArticleGoogle Scholar
- Gonzalez-Cinca N, Pérez de la Ossa P, Carreras J, Climent F: Effects of thyroid hormone and hypoxia on 2, 3-Bisphosphoglycerate, bisphosphoglycerate synthase and phosphoglycerate mutase in rabbit erythroblasts and reticulocytes in vivo. Horm Res. 2004, 62: 191-196. 10.1159/000080897View ArticlePubMedGoogle Scholar
- Hamaguchi T, Iizuka N, Tsunedomi R, Hamamoto Y, Miyamoto T, Iida M, Tokuhisa Y, Sakamoto K, Takashima M, Tamesa T, Oka M: Glycolysis module activated by hypoxia-inducible factor 1alpha is related to the aggressive phenotype of hepatocellular carcinoma. Int J Oncol. 2008, 33: 725-731.PubMedGoogle Scholar
- Gatenby RA, Gillies RJ: Why do cancers have high aerobic glycolysis?. Nat Rev Cancer. 2004, 4: 891-899. 10.1038/nrc1478View ArticlePubMedGoogle Scholar
- Liu R, Li Z, Bai S, Zhang H, Tang M, Lei Y, Chen L, Liang S, Zhao Y, Wei Y, Huang C: Mechanism of cancer cell adaptation to metabolic stress: proteomics identification of a novel thyroid hormone mediated gastric carcinogenic signaling pathway. Mol Cell Proteomics. 2009, 8: 70-85. 10.1074/mcp.M800195-MCP200View ArticlePubMedGoogle Scholar
- McFate T, Mohyeldin A, Lu H, Thakar J, Henriques J, Halim ND, Wu H, Schell MJ, Tsang TM, Teahan O, Zhou S, Califano JA, Jeoung NH, Harris RA, Verma A: Pyruvate dehydrogenase complex activity controls metabolic and malignant phenotype in cancer cells. J Biol Chem. 2008, 283: 22700-22708. 10.1074/jbc.M801765200PubMed CentralView ArticlePubMedGoogle Scholar
- Ortega AD, Sánchez-Aragó M, Giner-Sánchez D, Sánchez-Cenizo L, Willers I, Cuezva JM: Glucose avidity of carcinomas. Cancer Lett. 2009, 276: 125-135. 10.1016/j.canlet.2008.08.007View ArticlePubMedGoogle Scholar
- Mashima T, Seimiya H, Tsuruo T: De novo fatty-acid synthesis and related pathways as molecular targets for cancer therapy. Br J Cancer. 2009, 100: 1369-1372. 10.1038/sj.bjc.6605007PubMed CentralView ArticlePubMedGoogle Scholar
- Viollet B, Athea Y, Mounier R, Guigas B, Zarrinpashneh E, Horman S, Lantier L, Hebrard S, Devin-Leclerc J, Beauloye C, Foretz M, Andreelli F, Ventura-Clapier R, Bertrand L: AMPK: Lessons from transgenic and knockout animals. Front Biosci. 2009, 14: 19-44. 10.2741/3229View ArticleGoogle Scholar
- Board M, Humm S, Newsholme EA: Maximum activities of key enzymes of glycolysis, glutaminolysis, pentose phosphate pathway and tricarboxylic acid cycle in normal, neoplastic and suppressed cells. Biochem J. 1990, 265: 503-509.PubMed CentralView ArticlePubMedGoogle Scholar
- Gillies RJ, Gatenby RA: Adaptive landscapes and emergent phenotypes: why do cancers have high glycolysis?. J Bioenerg Biomembr. 2007, 39: 251-257. 10.1007/s10863-007-9085-yView ArticlePubMedGoogle Scholar
- Ko YH, Pedersen PL, Geschwind JF: Glucose catabolism in the rabbit VX2 tumor model for liver cancer: characterization and targeting hexokinase. Cancer Lett. 2001, 173: 83-91. 10.1016/S0304-3835(01)00667-XView ArticlePubMedGoogle Scholar
- Hanahan D, Weinberg RA: The hallmarks of cancer. Cell. 2000, 100: 57-70. 10.1016/S0092-8674(00)81683-9View ArticlePubMedGoogle Scholar
- Kasibhatla S, Tseng B: Why target apoptosis in cancer treatment?. Mol Cancer Ther. 2003, 2: 573-580.PubMedGoogle Scholar
- Kok JW, Sietsma H: Sphingolipid metabolism enzymes as targets for anticancer therapy. Curr Drug Targets. 2004, 5: 375-82. 10.2174/1389450043345452View ArticlePubMedGoogle Scholar
- Chi JT, Chang HY, Wang NN, Chang DS, Dunphy N, Brown PO: Genomewide view of gene silencing by small interfering RNAs. Proc Natl Acad Sci USA. 2003, 100: 6343-6. 10.1073/pnas.1037853100PubMed CentralView ArticlePubMedGoogle Scholar
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